Multiclonal human origin and global expansion of an endemic bacterial pathogen of livestock.

Most new pathogens of humans and animals arise via switching events from distinct host species. However, our understanding of the evolutionary and ecological drivers of successful host adaptation, expansion, and dissemination are limited. Staphylococcus aureus is a major bacterial pathogen of humans and a leading cause of mastitis in dairy cows worldwide. Here we trace the evolutionary history of bovine S. aureus using a global dataset of 10,254 S. aureus genomes including 1,896 bovine isolates from 32 countries in 6 continents. We identified 7 major contemporary endemic clones of S. aureus causing bovine mastitis around the world and traced them back to 4 independent host-jump events from humans that occurred up to 2,500 y ago. Individual clones emerged and underwent clonal expansion from the mid-19th to late 20th century coinciding with the commercialization and industrialization of dairy farming, and older lineages have become globally distributed via established cattle trade links. Importantly, we identified lineage-dependent differences in the frequency of host transmission events between humans and cows in both directions revealing high risk clones threatening veterinary and human health. Finally, pangenome network analysis revealed that some bovine S. aureus lineages contained distinct sets of bovine-associated genes, consistent with multiple trajectories to host adaptation via gene acquisition. Taken together, we have dissected the evolutionary history of a major endemic pathogen of livestock providing a comprehensive temporal, geographic, and gene-level perspective of its remarkable success.

A widespread family of WYL-domain transcriptional regulators co-localizes with diverse phage defence systems and islands.

Bacteria are under constant assault by bacteriophages and other mobile genetic elements. As a result, bacteria have evolved a multitude of systems that protect from attack. Genes encoding bacterial defence mechanisms can be clustered into 'defence islands', providing a potentially synergistic level of protection against a wider range of assailants. However, there is a comparative paucity of information on how expression of these defence systems is controlled. Here, we functionally characterize a transcriptional regulator, BrxR, encoded within a recently described phage defence island from a multidrug resistant plasmid of the emerging pathogen Escherichia fergusonii. Using a combination of reporters and electrophoretic mobility shift assays, we discovered that BrxR acts as a repressor. We present the structure of BrxR to 2.15 Å, the first structure of this family of transcription factors, and pinpoint a likely binding site for ligands within the WYL-domain. Bioinformatic analyses demonstrated that BrxR-family homologues are widespread amongst bacteria. About half (48%) of identified BrxR homologues were co-localized with a diverse array of known phage defence systems, either alone or clustered into defence islands. BrxR is a novel regulator that reveals a common mechanism for controlling the expression of the bacterial phage defence arsenal.

A graph-based approach for the visualisation and analysis of bacterial pangenomes.

BACKGROUND: The advent of low cost, high throughput DNA sequencing has led to the availability of thousands of complete genome sequences for a wide variety of bacterial species. Examining and interpreting genetic variation on this scale represents a significant challenge to existing methods of data analysis and visualisation. RESULTS: Starting with the output of standard pangenome analysis tools, we describe the generation and analysis of interactive, 3D network graphs to explore the structure of bacterial populations, the distribution of genes across a population, and the syntenic order in which those genes occur, in the new open-source network analysis platform, Graphia. Both the analysis and the visualisation are scalable to datasets of thousands of genome sequences. CONCLUSIONS: We anticipate that the approaches presented here will be of great utility to the microbial research community, allowing faster, more intuitive, and flexible interaction with pangenome datasets, thereby enhancing interpretation of these complex data.